microbial community design: methods, applications, and...

Microbial community design: methods, applications,and opportunitiesAlexander Eng1 and Elhanan Borenstein1,2,3,4,5

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Microbial communities can perform a variety of behaviors that

are useful in both therapeutic and industrial settings.

Engineered communities that differ in composition from

naturally occurring communities offer a unique opportunity for

improving upon existing community functions and expanding

the range of microbial community applications. This has

prompted recent advances in various community design

approaches including artificial selection procedures, reduction

from existing communities, combinatorial evaluation of

potential microbial combinations, and model-based in silico

community optimization. Computational methods in particular

offer a likely avenue toward improved synthetic community

development going forward. This review introduces each class

of design approach and surveys their recent applications and

notable innovations, closing with a discussion of existing

design challenges and potential opportunities for

advancement.

Addresses1Department of Genome Sciences, University of Washington, Seattle,

WA, 98195, USA2Department of Computer Science and Engineering, University of

Washington, Seattle, WA 98195, USA3Blavatnik School of Computer Science, Tel Aviv University, Tel Aviv

6997801, Israel4 Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv 6997801,

Israel5 Santa Fe Institute, Santa Fe, NM 87501, USA

Corresponding author: Borenstein, Elhanan ([email protected])

Current Opinion in Biotechnology 2019, 58:117–128

This review comes from a themed issue on Systems biology

Edited by Maria Klapa and Yannis Androulakis

https://doi.org/10.1016/j.copbio.2019.03.002

0958-1669/ã 0001 Elsevier Ltd. All rights reserved.

IntroductionMicrobial communities have long been recognized for the

important impact they have on human health, agriculture,

and industry. In the context of health, specifically, recent

studies have highlighted the key role that the human

microbiome – the ensemble of microorganisms that live in

and on the human body – plays in human physiology,

immunity, development, and nutrition. For example,

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microbially produced butyrate contributes to colonic

health via its function as an important energy source

[1] and anti-inflammatory agent [2]. Proper immune

system development also relies on the gut microbiome,

which modulates aspects such as lymphoid structure

development and T cell differentiation [3,4]. Beyond

human health, microbial activity is important to a range

of industrial applications including microbially mediated

denitrification in wastewater treatment [5,6] and biofuel

production [7,8]. These microbial influences also extend

to agriculture, where bacteria support plant access to

nitrogen [9,10] and phosphorus [11].

These myriad important functions suggest that microbial

communities can serve as a prime target for medical,

agricultural, and industrial advancement and have

sparked recent interest in developing microbiome-based

biotechnologies and therapeutics [12��,13]. Specifically,

microbiome engineering – the manipulation of naturally

occurring microbial communities or the construction of

synthetic communities to produce a specific function – is

a promising tool for improving and innovating upon

various clinical and industrial applications. Such attempts

to engineer a given community can be done by various

approaches. One simple microbiome engineering tech-

nique is the modulation of environmental conditions to

effect changes in community function. This approach is

frequently applied to optimize bioreactor communities,

which are used for the production and degradation of

various compounds. Indeed, previous studies have shown

that bioreactor operators can improve community func-

tion by changing substrate composition [14–16], aeration

[17], pH [14,18], and temperature [19]. Another simple

approach is to modify a community by adding beneficial

species or removing undesirable ones. Antibiotics are

perhaps the most prevalent example of this approach

in therapeutics, acting as a tool for removing pathogenic

species. Probiotics, in contrast, are an example of an

additive tool, aiming to improve gut community function

by introducing beneficial species [13,20,21].

Importantly, however, for microbiome engineering to

realize its full potential, more sophisticated techniques

must be employed to provide researchers with greater

control when engineering microbial communities. This is

especially important for the construction of synthetic

communities that consist of a human-developed mixture

of species. In the context of microbiome-based therapy,

such synthetic communities could enable more precise

modulation of the microbiome and bypass the negative


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118 Systems biology

side effects of antibiotics usage [22,23] or potential incon-

sistency of probiotic engraftment [24,25]. Synthetic com-

munities can also be useful for industrial applications,

where the novel coupling of microbial metabolisms can

lead to the improved production or degradation of eco-

nomically important compounds [26]. More generally,

synthetic communities provide a more flexible and pow-

erful engineering approach, allowing researchers to engi-

neer the community as a whole, rather than being limited

to the perturbation of an existing community. However,

one key challenge in developing such synthetic commu-

nities is the identification of novel species compositions

that optimize, or at least improve upon, desired functions.

This task, which can be thought of as ‘rational design’ for

community engineering, is far from trivial and various

techniques have been developed and applied to address

this need.

In this review, we survey a variety of approaches for

designing synthetic compositions with targeted functions,

highlighting recent methodological innovations and

applications. Importantly, we focus on methods for design-ing synthetic compositions (i.e. new mixtures of existing

microbial species), rather than the use of genetically

engineered microbes in synthetic compositions, which

has been reviewed elsewhere [27,28,29�,30]. We begin

with approaches that rely on selective pressures and

adaptation of community composition to reach synthetic

compositions with optimized functions. Next, we cover

methods that use microbial isolates from existing com-

munities to formulate reduced, well-defined composi-

tions that recapitulate the desired functions of the source

communities. We then describe techniques that evaluate

possible synthetic compositions in a combinatorial man-

ner to identify desirable compositions. Finally, we

describe the recently expanding range of computational

tools that identify candidate compositions predicted to

optimize a desired function. We conclude by discussing

the challenges and potential opportunities that are still

present in the field of synthetic community composition

design. Since therapeutic community design studies are

currently limited, we illustrate the application of certain

design approaches using examples from industrial

settings.

Community enrichment toward syntheticcompositionsNaturally occurring microbial communities can carry out

an amazing variety of functions, many of which could be

harnessed toward clinical, industrial, and environmental

applications. For example, the human gut microbiome

can perform multiple metabolic processes that are crucial

for the host, including harvesting energy from the diet

[31], synthesizing important vitamins [32], and resisting

pathogen colonization via competitive exclusion [33].

Indeed the transplantation of fecal microbiota from

healthy donors has proven effective at treating several


gastrointestinal disorders [13,34–36]. Similarly, different

soil communities can degrade various pollutants, such as

diesel fuel [37] and polycyclic aromatic hydrocarbons

[38], or prevent non-biodegradable pollutants, such as

uranium [39], from contaminating water supplies by

catalyzing their conversion to insoluble forms. However,

the efficiency at which naturally occurring communities

perform these functions may not be sufficient for indus-

trial settings, necessitating an optimization procedure

that can build upon such communities and ultimately

produce synthetic communities with enhanced capabili-

ties. A common approach to achieve this goal is through

enrichment — a community design methodology that aims

to reach a community composition with optimal desired

capabilities by subjecting an existing community to envi-

ronmental conditions that favor species that can perform

the target function. To date, enrichment has primarily

been used for biotechnological application, including

microbial fuel cells (MFCs), biopolymer production,

and biohydrogen production, which will be described

below.

MFCs have become a prime target of microbial commu-

nity engineering due to both the promise of efficient

microbially mediated electricity generation and the wide

range of substrates that they can utilize. This biotechnol-

ogy was originally inspired by marine sediment commu-

nities that reduce various elements for energy in a manner

that can be exploited to generate electricity [40–43].

Recent MFC applications can now utilize a wide range

of substrates (depending on the specific microbial com-

munity employed) including glucose [44,45], acetate [44],

lactate [46], cellulose [47,48], and ammonium [49].

Indeed, MFCs can even consume various industrial waste

products, enabling the coupling of electricity generation

with waste degradation [50–53]. Though naturally occur-

ring communities can already achieve these tasks, MFCs

seeded with such communities often undergo composi-

tional changes and exhibit gradual improvement in effi-

ciency over time as the community adapts to operating in

the MFC environment. Such changes were shown to

include enrichment for species potentially related to

current generation [54] and degradation of the supplied

substrate [55], and the observed changes in community

composition during extended operation of an MFC were

demonstrated to be linked to concurrent increases in

MFC efficiency [56].

Notably, while MFC communities experience inherent

and appropriate selective pressures, other biotechnolo-

gies may require the application of artificial selection

procedures to optimize community function. For exam-

ple, to increase the yield and efficiency of microbial

communities grown and harvested for biopolymers used

in biodegradable plastics, researchers have applied an

artificial feast-famine cycle [57,58]. This cycle selects

for communities that store energy (in the form of


Microbial community design Eng and Borenstein 119

biopolymers) more efficiently during the feast phase so

that energy is available during the famine phase. This

procedure can be further enhanced by introducing phos-

phate limitation, which can ensure that biopolymer pro-

duction is advantageous while also reducing the growth of

subcommunities that do not contribute to production

[59]. A recent work has also demonstrated that photosyn-

thetic communities can be enriched for biopolymer pro-

duction without a famine step when oxygen is limited

[60�].

Artificial selection procedures have also improved micro-

bial community hydrogen production, though in this

context, artificial section is often applied as a pretreat-

ment rather than as part of post-enrichment operating

conditions. Such pretreatments include heat shock, acidic

or basic incubation, freeze drying, and chloroform treat-

ment [61]. Each of these pretreatments aims to enrich for

hydrogen-producing species in the original community

while excluding hydrogen consumers. Interestingly,

though, the efficacy of different pretreatments is incon-

sistent across different studies, likely due to difference in

the set of species present in the initial community [62].

Consequently, the discovery of new promising hydrogen-

producing communities necessitates the re-evaluation of

these enrichment procedures to identify the best pre-

treatment [63].

Community reduction from existingcompositionsSome microbial community applications may impose

specific restrictions on the species that can be present

in the synthetic community. For example, microbiome

therapeutics must meet various regulatory guidelines

[64��], and be devoid of pathogenic species so as to avoid

inadvertently infecting the recipient [12��]. However, it

may not be possible to fully satisfy such restrictions using

enrichment approaches due to the relatively broad and

unspecified nature of environmental selection. For exam-

ple, applying environmental conditions that inhibit the

growth of pathogens may simultaneously negatively

impact the growth of desirable species. This challenge

can be addressed by a complementary design approach,

referred to in this review as community reduction, wherein

individual members of some initial community are iso-

lated and characterized to rationally determine whether

they should be used in the synthetic community. While

some community members may be lost during the isola-

tion step [65], this approach provides better control over

community composition and enables a more principled

selection of desirable species and the explicit exclusion of

undesirable ones.

This design paradigm has been used, for example, to

reconstruct synthetic communities for treating Clostridiumdifficile infection (CDI). CDI is a gastrointestinal infection

where C. difficile, a spore-forming, antibiotic-resistant


enteric pathogen, dominates the gut microbiome, causing

inflammation and diarrhea [66]. Previous clinical studies

have demonstrated that CDI can be effectively treated

with fecal microbiota transplantation (FMT) from a healthy

donor [35]. This makes CDI a prime target for synthetic

FMTs that could potentially recapitulate the same benefi-

cial effects using a simplified, well-defined community

composition. Indeed, an early study used a mixture of

ten previously identified and isolated intestinal species

to formulate a reduced synthetic CDI treatment composi-

tion [67]. In this study, all five patients treated with the

synthetic composition exhibited marked improvement,

similar to that observed in a patient treated with a donor

stool sample. Surprisingly, one of these five patients was

previously treated unsuccessfully with a donor stool trans-

plant, suggesting that reduced synthetic FMT composi-

tions may not be strictly inferior in efficacy to traditional

FMTs. In a more recent study, a synthetic FMT composi-

tion was designed by isolating as many individual species as

possible from a single donor’s stool [68��]. These isolates

were screened for pathogens, and the remaining non-path-

ogenic isolates were mixed to form a synthetic community.

Both patients treated with this synthetic community com-

position responded well to treatment, and longitudinal

sampling revealed notable engraftment of the species in

the synthetic composition, though this declined over time.

The community reduction approach has also shown

success when used for non-CDI therapeutic applica-

tions. For example, rather than targeting C. difficile,Caballero et al. investigated the role that gut species

play in resisting Vancomycin-resistant Enterococcus(VRE) colonization in mice [69]. As part of this larger

study, the authors isolated ampicillin-resistant strains

from mouse stool and examined which strains could

confer VRE resistance. From these experiments, they

identified a four-strain synthetic composition that both

resisted VRE colonization and ameliorated pre-estab-

lished VRE colonization. In another example, Atarashi

et al. set out to design a synthetic composition that would

induce Treg cells in the mouse colon [70]. The authors

isolated species from a human donor’s chloroform-trea-

ted stool and found that a synthetic community com-

posed of 17 isolates induced Treg cells in germ-free mice

to a similar extent as the original chloroform-treated

stool. The same group, as part of a larger study of

intestinal Th17 cell induction, also used community

reduction to successfully identify a synthetic composi-

tion of 20 human gut strains with notable Th17 cell

induction in mice [71]. These examples illustrate the

potential of community reduction to recapitulate various

important gut microbiome functions, making it a prom-

ising tool for future microbiome therapeutics. One

important caveat to note, however, is that community

reduction inherently cannot design synthetic composi-

tions with novel functions, thus restricting its wider

applicability.


120 Systems biology

Combinatorial evaluation of potentialcompositionsOne of the unique benefits of synthetic communities is that

they can include combinations of species that never co-

occur in naturally occurring communities, potentially facil-

itating a wider range of metabolic capacities.Such synthetic

communities may therefore be able to perform certain

functions better than existing communities (or communi-

ties obtained via enrichment or reduction) or even perform

entirely new functions. This is of particular interest for

industrial applications such as the production of biofuel and

other biological compounds [26]. Indeed, exploration of

novel metabolic coupling in synthetic communities has

already proved successful, demonstrating potential appli-

cations for the production of various resources including

hydrogen [72], acetic acid [73–75], and lactic acid [76,77], as

well as the degradation of undesirable substances including

polycyclic aromatic hydrocarbons [78] and cellulose

[79,80]. To go beyond a simple trial and error exploration

approach for identifying such beneficial combinations,

researchers can employ a more comprehensive process,

referred to in this review as combinatorial evaluation, the

systematic enumeration, construction, and evaluation of

possible combinations of a set of species to identify the

best-performing composition. The set of species used

could, for example, consist of candidate species that are

believed to contribute to the desired function.

When the number of species to consider is small, combi-

natorial evaluation can be performed in its ideal form,

constructing and assessing all possible combinations of

the species of interest. For example, to optimize the

biodegradation of dyes in textile wastewater, researchers

isolated three species from a textile wastewater plant and

evaluated the degradation capabilities of all combinations

of these three species [81]. In fact, because of the rela-

tively small number of species considered, they were also

able to evaluate additional compositions that varied in the

relative abundances of each species.

Importantly, however, as the number of candidate species

grows, the number of potential compositions grows expo-

nentially, quickly rendering the evaluation of all possible

combinations impossible. This setting calls for techniques

that can drastically reduce the number of evaluated com-

positions. One such technique is fractional factorial design

(FFD). In general terms, FFD aims to estimate the effects

of, and potentially the interactions between, particular

components of a system on a specified output [82]. These

effect and interaction measurements then provide a basis

for mathematically identifying an optimal parameterization

of these components. In the context of microbial commu-

nity design, FFD reduces the required numberof evaluated

compositions by carefully selecting a subset of potential

community compositions that can isolate specific species

effects or interactions of interest. One important caveat is

that FFD achieves this reduction in evaluated compositions


by assuming negligible effects of higher-order interactions.

However, if later evidence suggests thatone ormorehigher-

order interactions have important contributions, a tech-

nique called foldover design can be applied to efficiently

determine those specific interaction effects based on the

findings of the original FFD experiment [82].

Microbial community function optimization via FFD has

historically focused on factors external to the community.

Forexample,various studieshaveusedFFD toexamine the

impact of environmental factors such as substrate composi-

tion [15,37,83–85], pH [83,84,86], temperature [83], and

heavy metal presence[86] on specific community functions.

More recent efforts, however, have used FFD, or FFD-like

techniques, to investigate thepotentialof individualspecies

effect estimation. A recent study, for example, has used

random gut community subsets to estimate individual

microbial contributions to host phenotypes [87�]. Though

these compositions were randomly selected (rather than

specifically constructed to most efficiently separate indi-

vidual contributions), the results of this study suggest that

FFD could be applied in a similar manner for synthetic

composition optimization by treating species as the com-

ponents of interest. Indeed, one group has already applied

FFD to develop wastewater treatment communities

[88,89]. In this pair of studies, the authors used FFD to

estimate the contributions of both individual species and

interspecies interactions to total organic carbon (TOC)

degradation [88] and substrate utilization rate [89]. They

then employed this information to develop synthetic com-

positions with improved biodegradation capacities. Inter-

estingly, both studies found that the optimized three-strain

and four-strain communities performed better than a base-

line mixture of all six strains evaluated, demonstrating the

utility of FFD in synthetic community design.

Another technique for efficiently evaluating potential

compositions is the definition of microbial consortia that

will be treated as single units when enumerating possible

species combinations (i.e. each combination will either

include or exclude all species in a given consortium). This

technique is particularly useful when a microbial consor-

tium has previously demonstrated a desirable emergent

function. For example, one group observed that a consor-

tium of marine species, named the NPMC, could effi-

ciently fix CO2 [90]. They later treated this consortium as

a single candidate community member when using a

combinatorial evaluation approach to develop a synthetic

community for CO2 fixation [91�]. Importantly, in addi-

tion to reducing the pool of available species to six

candidate community members (one of which was the

NPMC), this approach also allowed the researchers to

include species that could not be isolated from the

NPMC in the final community. In a separate study,

the same group designed a synthetic community for

lignocelluloytic enzyme activity by considering a syn-

thetic consortium previously designed for cellulolytic



activity [80] alongside several fungal strains [92]. These

studies highlight the benefits of this approach, allowing

researchers to evaluate designed communities with

higher complexity without drastically increasing the num-

ber of evaluated compositions.

Computational model-based design ofsynthetic compositionsThe design paradigms described above rely on various

approaches for characterizing and assessing candidate com-

munity compositions, with techniques such as FFD and

consortium inclusion allowing researchers to reduce the set

of compositions ultimately evaluated. Importantly, how-

ever, such approaches may still entail evaluating many

compositions that a priori might be expected to perform

the desired function poorly based on existing knowledge of

microbial ecology, genomics, and metabolism. Indeed,

databases such as NCBI [93] and IMG [94] provide access

to an ever increasing number of sequenced microbial

genomes, which when coupled with various gene annota-

tion databases, such as KEGG [95] and MetaCyc [96], can

be used to infer the functional capacities of individual

microbial species. Design methodologies that could har-

ness such information to pinpoint community compositions

that are likely to successfully and efficiently perform the

desired function might dramatically reduce the time and

labor needed for experimental evaluation. As a simple

example, a collection of previously sequenced and geno-

mically annotated microbial species could be searched to

identify species whose genomes confer the capacity to

perform a certain function, even when these species have

never been experimentally tested for that function in the

lab. In this review, however, we will focus on more sophis-

ticated computational methods for designing synthetic

community compositions, primarily highlighting methods

that aim to model community-level metabolism and to

identify synthetic compositions that are predicted to per-

form the desired function well. We refer to this approach as

computational model-based design.

Microbial community metabolism can be modeled with

varying levels of complexity andusing a variety of modeling

frameworks [97,98]. A relatively simple form of metabolic

modeling, often referred to as network-based or topology-

based modeling, represents each species as a directed

network where nodes denote metabolites and edges con-

nect substrates to products, reflecting the set of metabolic

reactions the modeled species can catalyze. With this

framework, community metabolism can be modeled as a

collection of such networks, where outputs from one net-

workcanbeused as input for another.A recently introduced

design algorithm, termed CoMiDA [99�], has utilized this

modeling framework to identify a minimal set of microbial

species that collectively provide the enzymatic capacity

required to synthesize a set of desired products from a

predefined set of available substrates. To achieve this, the

CoMiDA algorithm integrates a graph-theoretic


representation of network flow with the set cover problem

to consider all possible metabolic paths from substrates to

products and to detect the minimal set of species that can

catalyze these reactions. The obtained solution can provide

a starting point for further synthetic community experi-

mentation and development. Another design algorithm,

termed MultiPus [100], utilizes a similar framework but

aims to minimize the number of reactions and inter-micro-

bial transfers, rather than the number of species.

While network-based models are easy to construct and

analyze, they generally only account for the potential

metabolic capacity of each species, rather than for the

way each species will behave in a given environment.

Accordingly, communities designed by CoMiDA or Multi-

Pus are indeed guaranteed to have the metabolic potential tocarry out the desired function, but may not actually perform

this function in reality. Instead, the accurate estimation of

microbiome behavior requires a detailed model of micro-

bial metabolism, one that can predict the specific activity of

each species, the flux through each reaction, the uptake and

secretion rate of environmentally available metabolites,

and the growth rate of each species in a given environment.

One such modeling framework utilizes constraint-based

models and flux balance analysis (FBA) [101,102��]. Such

models can predict the steady state metabolic activity of a

given species by identifying a set of metabolic fluxes that

maximize microbial growth while adhering to a set of

thermodynamic constraints [103,104]. Recent years have

witnessed an explosion of studies that aim to extend con-

straints-based modeling from single species models to

community models that can predict community-level

metabolism, species interactions, and community dynam-

ics [105–108]. Building on these effort, a recent design

method, termed FLYCOP [109��], has utilized a previously

introduced community modeling framework to evaluate

synthetic composition function in silico. Importantly, the

underlying modeling framework accounts for community

dynamics and spatial community organization, as well as

metabolism-mediated species-interactions via changes to

the shared environment [107]. Using this framework, FLY-

COP explores potential synthetic compositions using a

stochastic search procedure and identifies an optimized

composition. Interestingly, FLYCOP is not restricted to

optimizing metabolic activity, and can also consider a

community’s growth over time. This allowed the authors

to identify an initial synthetic composition of four cross-

feeding strains that optimized community stability. This

ability to optimize community stability could be extremely

important for therapeutics, where treatment may require

the community to function for a prolonged period.

Another modeling approach that can be useful for com-

munity design efforts, especially when stability is an

important consideration, aims to model community ecologi-

cal dynamics rather than community metabolism. These

models capture how the abundance of each community


122

Systems

biology

Table 1

Summary of synthetic community design methods

Design approach Description Requirements Benefits Limitations Examples of usage

Enrichment Environmental conditions are

modified to promote the

growth of species that

perform a desired function

� Initial community with capacity for

the desired function

� Selection procedure that favors

growth of species that can perform


� Optimizes community

function without identify-

ing individual species

involved

� Undefined or partially

defined communities may

be unsuited for therapeutics

� Optimization time can be

extensive depending on

the application and initial

community

Fradinho et al. [60�]Korkakaki et al. [59]

Kumari et al. [63]

Community

reduction

Species are isolated from an

existing community,

screened to keep desirable

species and/or exclude

undesirable species, and

then reconstituted into a

simplified community that

recapitulates the source

community’s function

� Initial community with capacity for


� Appropriate media for culturing

microbial isolates

� Screening procedures for isolated

species

� Produces a defined syn-

thetic composition

� Can explicitly exclude

pathogenic or otherwiseundesirable species

� Cannot design commu-

nities for novel functions

� May fail if important spe-

cies cannot be cultured

Petrof et al. [68��]Atarashi et al. [71]

Caballero et al. [69]

Combinatorial

evaluation

All possible combinations of

a set of candidate species

are evaluated for their

performance of a desired

function and the best-

performing composition is

selected

� Microbial isolates suspected to

contribute to the desired function

� Screening procedures for candidate

communities

� Produces a defined syn-

thetic composition

� Can explicitly exclude

pathogenic or otherwise

undesirable species

� Can combine species from

distinct sources

� Number of potential com-

positions to evaluate grows

exponentially with the

number of candidate

species

Hu et al. [91�]Hu et al. [92]

Curre

nt

Opinion

in B

iotechnology

2019,

58:117–128

www.sciencedire

ct.c

om


Table

1(C

ontinued)

Designapproach

Description

Requirements

Benefits

Lim

itations

Examplesofusage

Computational

model-based

design

Mechanisticmodels

of

microbialfunctional

capacitiesandactivitiesand/

orecologicalmodels

of

communitydynamicsare

usedto

evaluate

potential

communitycompositionsin

silico,identifyingoneormore

optimizedcompositionsfor

furtherexperimental

valid

ation

�Mechanisticknowledgeofindivi-

dual

microbialfunctionand/or

�Ecological

modelsofcommunity

dynam

ics

�Produces

adefined

syn-

thetic

composition

�Can

explicitly

exclude

pathogenic

or

otherwise

undesirable

species

�Can

combinespecies

from

distinct

source

s

�Reduces

time

and

labor

required

forexperim

ental

evaluation

�Well-curated

mechanistic

inform

ation

isunavailable

formanyspecies

�Mechanisticmodelsof

non-metab

olic

func

tion

sarelacking

EngandBorenstein

[99� ]

Julien-Laferriere

etal.[100]

Garcıa-Jim

enezetal.[109��]

Stein

etal.[112��]


member impacts the abundances of others over time

[110�,111]. Such models can be especially useful when

interactions between species may not be mediated via

metabolism or when detailed metabolic models are not

available. In one example, a group optimized a synthetic

composition for both a non-metabolic function and commu-

nity stability through a combination of ecological modeling

and experimental characterization of individual microbial

activity [112��]. In this case, the authors aimed to develop a

community for Treg induction in the mouse colon that would

persist over time. To achieve this, the authors first created a

model of community induction effectiveness for a set of

Clostridia strains using data on Treg induction contributions.

They then simulated the community’s ecological dynamics

using a previously published ecological model for those

strains [110�], and used their induction model to estimate

Treg induction over time. This enabled them to predict each

potential composition’s inductive effect and stability simul-

taneously. Such integration of different modeling frame-

work may be a promising avenue for future expansion and

improvement of computational design capabilities.

Synthetic composition design challenges andopportunitiesThe previous sections have surveyed recent applications

of, and advances in, synthetic composition design. Impor-

tantly, however, there are still many daunting challenges,

as well as exciting opportunities, for future development

in this field. Clearly, each design approach described

above has its own strengths and weaknesses that make

it more suitable for certain applications and less appro-

priate for others and that impose a specific set of chal-

lenges and opportunities (Table 1). Enrichment can often

work without detailed knowledge of individual species,

instead relying on understanding the desired biological

process in order to create a selection procedure. However,

each novel application may require a completely new

selection procedure, which could be challenging to

develop [113]. Additionally, the time required for opti-

mization can be extensive, with some experiments show-

ing continued improvement over the course of months

[58,114]. The community reduction and combinatorial

evaluation approaches similarly avoid the need for

detailed mechanistic data while also incorporating greater

control over the specific species used. Unfortunately, due

to our inability to culture a large fraction of microbial

species [65], community reduction can suffer from a

failure to isolate a set of species sufficient to recapitulate

the original community’s function. Additionally, commu-

nity reduction does not inherently optimize a synthetic

composition’s function, but rather only identifies a well-

defined synthetic composition with the desired function.

Combinatorial evaluation, in contrast, can suffer from

tractability issues regarding the number of compositions

to evaluate, as described above. Finally, computational

model-based design can drastically decrease the time and

labor needed to identify optimal, or near-optimal


124 Systems biology

compositions, but it requires detailed and thorough mech-

anistic and/or ecological data about the species being

considered. Understanding these differences between

approaches, as well as considering the available time,

labor, and knowledge resources, will help future

designers select the approach best suited for their specific

application.

While each design technique offers unique benefits, the

power and flexibility of computational model-based design,

combined with the recent expansion of available genomic,

metabolic, and other mechanistic data, render such compu-

tational design efforts an especially promising route toward

rapid advancement in synthetic community design. For

example, methods that can concurrently optimize multiple

community functions could enable synthetic therapeutic

communities to simultaneously treat diverse health con-

cerns, such as metabolic deficiencies and pathogenic infec-

tions, while also ensuring community stability. There are,

however, substantial obstacles that must be overcome for

model-based design to reach its full potential. One key

challenge is the inability of many currently modeling

frameworks to directly incorporate knowledge of non-met-

abolic microbial interactions into models of community

function, which recent evidence suggests can be important

factors in shaping the human gut microbiome [115]. Eco-

logical models that are based on observed community

dynamics may only partially capture the outcomes of such

interactions, and it is likely that the nuanced effects of key

interaction mediators are ignored [116�]. The potentially

important functional impact of higher-order interactions (i.

e. interactions involving multiple species in the commu-

nity) [117–119] poses another challenge for computational

methods, specifically when such methods evaluate only a

subset of possible compositions. This calls for more sophis-

ticated methods thatefficiently searchthe space ofpotential

compositions while adequately accounting for higher-order

interactions, which would be especially advantageous.

Perhaps the most promising avenue for advancement in

synthetic design might entail the integration of multiple

design approaches such that the weaknesses of one

approach would be addressed by the incorporation of a

complementary strategy. Indeed, there is already evidence

that communities designed using one method can be fur-

ther improved via orthogonal design techniques. The com-

putationally designed synthetic Treg induction community

described above [112��] was developed from a Treg induc-

tion community originally designed using community

reduction [70]. In this case, community reduction identified

a set of culturable species that could form a synthetic Treg

induction community and computational design optimized

the composition to improve its function. Pairing enrichment

with combinatorial evaluation or model-based design could

also offer potentially fruitful composite approaches. Specif-

ically, the enrichment procedure could begin with compo-

sitions identified and pre-optimized by other design


techniques, rather than environmentally sampled or ran-

domly constructed initial communities. Such a method

integration could potentially reduce the time required to

select for an optimal composition since the initial commu-

nity is hopefully closer in composition to the final commu-

nity thatenrichmentwouldachieve.Additionally, thiscould

help augment combinatorial evaluation or computational

design, which may result in suboptimal communities due to

insufficient coverage of evaluated compositions or insuffi-

cient mechanistic and ecological knowledge respectively.

Such innovative combinations of design approaches could

enhance or enable the development of synthetic composi-

tions that may have previously been challenging due to the

various limitations of each individual approach.

ConclusionsIn this review, we have surveyed various methods for design-

ing synthetic microbial communities, highlighting their util-

ity in formulating and optimizing communities for a wide

variety of applications. These methods range from experi-

mentally driven techniques, including enrichment, commu-

nity reduction, and combinatorial evaluation, to computa-

tional approaches that employ mechanistic models of

microbial function and ecological models of community

dynamics. We have described various successful applications

of these methods to both industrial and therapeutic synthetic

community design, noting interesting and important obser-

vations made during the design process. Perhaps the most

important of these observations is that optimized communi-

ties are often smaller and less complex than naturally occur-

ring communities. Indeed, as in some cases mentioned

above, simpler synthetic communities with fewer strains

have achieved better performance than their more complex

counterparts. This suggests that design methods will con-

tinue to play an important role in identifying the particular

subcommunities that best achieve specific functions. Given

this, we believe that continued advancement in all classes of

design approaches will greatly expand the uses of, and

improve the efficacy of, synthetic microbial communities.

Conflict of interest statementNothing declared.

AcknowledgementsWe thank the members of the Borenstein Lab for helpful feedback. Thiswork was supported by the NIH New Innovator Award DP2AT00780201and by the N.I.H. grant 1R01GM124312–01 to EB.

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http://dx.doi.org/10.1101/232959

http://dx.doi.org/10.1101/232959

http://dx.doi.org/10.1101/333534

http://dx.doi.org/10.1101/333534

microbial community design: methods, applications, and...

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